bitwise_or (ivy-llc#9861)

* bitwise_or

Close ivy-llc#9848

* bitwise_or

Close ivy-llc#9848

* Update test_tensor.py

* Update test_tensor.py

* Update tensor.py

* Update tensor.py

ivy/functional/frontends/torch/tensor.py

@@ -256,6 +256,9 @@ def abs_(self):
     def bitwise_and(self, other):
         return torch_frontend.bitwise_and(self._ivy_array, other)
 
+    def bitwise_or(self, other, *, out=None):
+        return torch_frontend.bitwise_or(self._ivy_array, other)
+
     def contiguous(self, memory_format=None):
         return torch_frontend.tensor(self.ivy_array)
 

ivy_tests/test_ivy/test_frontends/test_torch/test_tensor.py

@@ -2778,6 +2778,46 @@ def test_torch_instance_bitwise_and(
     )
 
 
+# bitwise_or
+@handle_frontend_method(
+    class_tree=CLASS_TREE,
+    init_tree="torch.tensor",
+    method_name="bitwise_or",
+    dtype_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("integer"),
+        num_arrays=2,
+    ),
+)
+def test_torch_instance_bitwise_or(
+    dtype_and_x,
+    init_num_positional_args: pf.NumPositionalArgFn,
+    method_num_positional_args: pf.NumPositionalArgMethod,
+    as_variable: pf.AsVariableFlags,
+    native_array: pf.NativeArrayFlags,
+    frontend_method_data,
+    frontend,
+):
+    input_dtype, x = dtype_and_x
+    helpers.test_frontend_method(
+        init_input_dtypes=input_dtype,
+        init_as_variable_flags=as_variable,
+        init_num_positional_args=init_num_positional_args,
+        init_native_array_flags=native_array,
+        init_all_as_kwargs_np={
+            "data": x[0],
+        },
+        method_input_dtypes=input_dtype,
+        method_as_variable_flags=as_variable,
+        method_num_positional_args=method_num_positional_args,
+        method_native_array_flags=native_array,
+        method_all_as_kwargs_np={
+            "other": x[1],
+        },
+        frontend_method_data=frontend_method_data,
+        frontend=frontend,
+    )
+
+
 # add_
 @handle_frontend_method(
     class_tree=CLASS_TREE,